3 Ways to improve candidate experience

Every employer wants their recruitment process to leave a good impression with candidates, even those who aren’t selected. The impact of not doing so can be far-reaching, particularly when an organisation has high volumes of applicants or in a situation in which candidates might also be customers. In this article, we share a few key ideas to help any employer ensure they deliver a great experience for candidates.

19th January 2017

Laura.Parrack

By James Lewis, Consultant, Cubiks UK

1. Personalised candidate feedback

It’s no longer good enough to only offer performance feedback to the successful candidates at the end of a selection process. A generic email to unsuccessful candidates that says something like ‘due to high volumes of applications received, we can’t offer any feedback whatsoever, but good luck next time’ isn’t feedback, and it isn’t a nice experience for candidates. Perhaps it’s better than nothing, but that shouldn’t be the benchmark for a ‘good candidate experience’.

The benchmark should be - how does our candidate experience ensure the highest-calibre candidates choose us over our competitors? Candidates deserve to get something back from the process simply due to the time they have invested in applying, let alone the fact that they could also be an existing or future valued customer.

Candidates will find value in receiving developmental guidance from your expert tools or assessors to help them learn and be more successful in their subsequent career. The technology exists today to automate feedback at any stage of the selection process, including high-volume sifting such as the ever popular Situational Judgement Test (SJT).

The good news is that this technology is also getting cheaper for employers as test providers are doing it more and getting better at it. Many assessment systems are now able to deliver automated email reports in a candidate friendly format to make this job easier.

These reports can be configured to offer development tips and insight into perceived strengths which align to the organisation’s unique values. Further, they can be aligned with interviews to create the opportunity for candidates to further validate their responses with behavioural evidence.

2. Gamifying the process

Gamification has been a buzz word over the past few years. For us, gamification should be about taking elements of the gaming experience and applying it to enhanced, psychometrically valid selection tools. It’s as much about Usability as it is about Gaming. This doesn’t mean playing angry birds and drawing loose inferences about a candidate’s accuracy. It means looking at the world of gaming and making our existing psychometric solutions more accessible.

Candidates today are used to Apps and highly Apple/Android driven online experiences - seamless, touch-based interfaces. How can these aspects of functionality improve our solutions, and subsequently the candidate experience? Test providers are now offering new ways to complete assessments, using touch screen or up and down arrows to rapidly select items – similar to basic gaming functionality, with benefits for ease of completion and speed of completion for candidates.

3. Add People Analytics

Big data? Little data? However you look at it, you collect a lot of candidate data during the assessment process. Whilst using this data to assess candidates’ fit and capability to perform the role, and also to give automated feedback, is pretty crucial, what more can it be used for?

Firstly, the gap between the candidate experience during the selection process and their employee experience in their new role could be reduced by using available data. There are opportunities to guide pre- and early role development and onboarding by looking at assessment performance in detail and exploring what strengths a candidate demonstrated that could enable them to have a greater impact from day 1.

Secondly, we can think about how data is used to shape a candidate’s unique selection experience. For example, contextual assessment is a growing field with algorithms which use data on a candidate’s educational opportunities and subsequent performance (e.g. they went to a poorly rated school but achieved high A-level grades, certainly higher than their schools average) to adjust their required levels of performance in sifting tools and selection exercises.